#' @title
#' Create \code{trans_abund} object for taxonomic abundance visualization.
#'
#' @description
#' This class is a wrapper for the taxonomic abundance transformations and visualization (e.g., bar plot, boxplot, heatmap, pie chart and line chart).
#' The converted data style is the long-format for \code{ggplot2} plot.
#'
#' @export
trans_abund <- R6Class(classname = "trans_abund",
	public = list(
		#' @param dataset default NULL; the object of \code{\link{microtable}} class.
		#' @param taxrank default "Phylum"; taxonomic level, i.e. a column name in \code{tax_table} of the input object.
		#'   The function extracts the abundance from the \code{taxa_abund} list according to the names in the list. 
		#'   If the \code{taxa_abund} list is NULL, the function can automatically calculate the relative abundance to generate \code{taxa_abund} list.
		#' @param show default 0; the mean relative abundance threshold for filtering the taxa with low abundance.
		#' @param ntaxa default 10; how many taxa are selected to use. Taxa are ordered by abundance from high to low. 
		#'   This parameter does not conflict with the parameter \code{show}. Both can be used. \code{ntaxa = NULL} means the parameter will be invalid.
		#' @param groupmean default NULL; calculate mean abundance for each group. Select a column name in \code{microtable$sample_table}.
		#' @param group_morestats default FALSE; only available when \code{groupmean} parameter is provided; 
		#'   Whether output more statistics for each group, including min, max, median and quantile;
		#'   Thereinto, quantile25 and quantile75 denote 25\% and 75\% quantiles, respectively.
		#' @param delete_taxonomy_lineage default TRUE; whether delete the taxonomy lineage in front of the target level.
		#' @param delete_taxonomy_prefix default TRUE; whether delete the prefix of taxonomy, such as "g__".
		#' @param prefix default NULL; character string; available when \code{delete_taxonomy_prefix = T}; 
		#'   default NULL represents using the "letter+__", e.g. "k__" for Phylum level;
		#'   Please provide the customized prefix when it is not standard, otherwise the program can not correctly recognize it.
		#' @param use_percentage default TRUE; whether show the abundance percentage. If TRUE, the abundance data will be multiplied by 100.
		#' @param input_taxaname default NULL; character vector; input taxa names to select some taxa.
		#' @param high_level default NULL; a taxonomic rank, such as "Phylum", used to add the taxonomic information of higher level.
		#'   It is required for the legend with nested taxonomic levels in the bar plot or the higher taxonomic level in facets of y axis in the heatmap.
		#' @param high_level_fix_nsub default NULL; an integer, used to fix the number of selected abundant taxa in each taxon from higher taxonomic level.
		#'   If the total number under one taxon of higher level is less than the high_level_fix_nsub, the total number will be used.
		#'   When \code{high_level_fix_nsub} is provided, the taxa number of higher level is calculated as: \code{ceiling(ntaxa/high_level_fix_nsub)}.
		#'   Note that \code{ntaxa} means either the parameter \code{ntaxa} or the taxonomic number obtained by filtering according to the \code{show} parameter.
		#' @return \code{data_abund} stored in the object. The column 'all_mean_abund' represents mean relative abundance across all the samples.
		#'   So the values in one taxon are all same across all the samples.
		#'   If the sum of column 'Abundance' in one sample is larger than 1, the 'Abundance', 'SD' and 'SE' has been multiplied by 100.
		#' @examples
		#' \donttest{
		#' data(dataset)
		#' t1 <- trans_abund$new(dataset = dataset, taxrank = "Phylum", ntaxa = 10)
		#' }
		initialize = function(
			dataset = NULL, 
			taxrank = "Phylum", 
			show = 0, 
			ntaxa = 10, 
			groupmean = NULL,
			group_morestats = FALSE,
			delete_taxonomy_lineage = TRUE,
			delete_taxonomy_prefix = TRUE,
			prefix = NULL,
			use_percentage = TRUE, 
			input_taxaname = NULL,
			high_level = NULL,
			high_level_fix_nsub = NULL
			){
			check_microtable(dataset)
			check_taxa_abund(dataset)
			if(! taxrank %in% names(dataset$taxa_abund)){
				stop("The input taxrank: ", taxrank, " is not found! Please check it!")
			}
			sample_table <- dataset$sample_table
			if("Sample" %in% colnames(sample_table)){
				colnames(sample_table)[colnames(sample_table) == "Sample"] <- "Sample_replace"
			}
			abund_data <- dataset$taxa_abund[[taxrank]] %>% 
				rownames_to_column(var = "Taxonomy") %>% 
				reshape2::melt(id.vars = "Taxonomy") %>% 
				`colnames<-`(c("Taxonomy", "Sample", "Abundance"))
			check_nd <- grepl("__$", abund_data$Taxonomy)
			if(any(check_nd)){
				abund_data$Taxonomy[check_nd] %<>% paste0(., "unidentified")
			}
			if(delete_taxonomy_lineage | delete_taxonomy_prefix){
				if(delete_taxonomy_prefix){
					if(is.null(prefix)){
						prefix <- ".__"
					}
					abund_data$Taxonomy %<>% gsub(prefix, "", .)
				}
				if(delete_taxonomy_lineage){
					abund_data$Taxonomy %<>% gsub(".*\\|", "", .)
				}
			}
			abund_data %<>% dplyr::group_by(!!! syms(c("Taxonomy", "Sample"))) %>% 
				dplyr::summarise(Abundance = sum(Abundance)) %>%
				as.data.frame(stringsAsFactors = FALSE)
			abund_data$Taxonomy %<>% as.character
			mean_abund <- tapply(abund_data$Abundance, abund_data$Taxonomy, FUN = mean)
			# add mean abundance for all samples
			all_mean_abund <- data.frame(Taxonomy = names(mean_abund), all_mean_abund = mean_abund)
			rownames(all_mean_abund) <- NULL
			abund_data %<>% {suppressWarnings(dplyr::left_join(., rownames_to_column(sample_table), by = c("Sample" = "rowname")))}
			if(!is.null(groupmean)){
				message(groupmean, " column is used to calculate mean abundance ...")
				abund_data <- microeco:::summarySE_inter(abund_data, measurevar = "Abundance", groupvars = c("Taxonomy", groupmean), more = group_morestats)
				colnames(abund_data)[colnames(abund_data) == "Mean"] <- "Abundance"
				colnames(abund_data)[colnames(abund_data) == groupmean] <- "Sample"
				if(is.factor(sample_table[, groupmean])){
					abund_data$Sample %<>% factor(., levels = levels(sample_table[, groupmean]))
				}
			}
			abund_data <- dplyr::left_join(abund_data, all_mean_abund, by = c("Taxonomy" = "Taxonomy"))
			if(!is.null(high_level)){
				if(length(high_level) > 1){
					warning("Input high_level has multiple elements! Only select the first one!")
					high_level <- high_level[1]
				}
				message("Add higher taxonomic level into the table ...")
				if(! high_level %in% colnames(dataset$tax_table)){
					stop("Provided high_level must be a column name of input dataset$tax_table!")
				}else{
					if(identical(high_level, taxrank)){
						stop("Provided high_level should not be same with taxrank !")
					}
					if(delete_taxonomy_lineage){
						extract_tax_table <- dataset$tax_table[, c(high_level, taxrank)] %>% unique
					}else{
						tmp_tax_table <- dataset$tax_table
						tmp_tax_table[, taxrank] <- apply(tmp_tax_table[, 1:match(taxrank, colnames(tmp_tax_table))], 1, function(x){paste0(x, collapse = "|")})
						extract_tax_table <- tmp_tax_table[, c(high_level, taxrank)] %>% unique
					}
					if(delete_taxonomy_prefix){
						extract_tax_table[, taxrank] %<>% gsub(prefix, "", .)
					}
					abund_data <- dplyr::left_join(abund_data, extract_tax_table, by = c("Taxonomy" = taxrank))
				}
			}
			use_taxanames <- as.character(rev(names(sort(mean_abund))))
			if(!is.null(ntaxa)){
				ntaxa_theshold <- ntaxa_use <- ntaxa
			}else{
				ntaxa_theshold <- ntaxa_use <- sum(mean_abund > show)
			}
			if(ntaxa_use > sum(mean_abund > show)){
				ntaxa_use <- sum(mean_abund > show)
			}
			use_taxanames %<>% .[!grepl("unidentified|unculture|Incertae.sedis", .)]
			if(is.null(input_taxaname)){
				if(is.null(high_level_fix_nsub)){
					if(length(use_taxanames) > ntaxa_use){
						use_taxanames %<>% .[1:ntaxa_use]
					}
				}else{
					high_level_n <- ceiling(ntaxa_use/high_level_fix_nsub)
					high_level_ordered_taxa <- abund_data[match(names(mean_abund), abund_data$Taxonomy), high_level] %>% unique %>% .[1:high_level_n]
					use_taxanames <- lapply(high_level_ordered_taxa, function(x){
						tmp <- abund_data[abund_data[, high_level] == x, ]
						tmp <- names(mean_abund) %>% .[. %in% tmp$Taxonomy]
						tmp[1:ifelse(length(tmp) < high_level_fix_nsub, length(tmp), high_level_fix_nsub)]
					}) %>% unlist
				}
			}else{
				if(!any(input_taxaname %in% use_taxanames)){
					stop("The input_taxaname does not match to taxa names! Please check the input!")
				}else{
					use_taxanames <- input_taxaname[input_taxaname %in% use_taxanames]
				}
			}
			if(!is.null(high_level)){
				# sort the taxa in high levels according to abundance sum
				tmp <- abund_data[abund_data$Taxonomy %in% use_taxanames, c(high_level, "Abundance")]
				tmp <- tapply(tmp$Abundance, tmp[, high_level], FUN = sum)
				data_taxanames_highlevel <- as.character(names(sort(tmp, decreasing = TRUE)))
				self$data_taxanames_highlevel <- data_taxanames_highlevel
			}
			if(ntaxa_theshold < sum(mean_abund > show) | show == 0){
				if(use_percentage == T){
					abund_data$Abundance %<>% {. * 100}
					if("SE" %in% colnames(abund_data)) abund_data$SE %<>% {. * 100}
					if("SD" %in% colnames(abund_data)) abund_data$SD %<>% {. * 100}
					ylabname <- "Relative abundance (%)"
				}else{
					ylabname <- "Relative abundance"
				}
			}else{
				ylabname <- paste0("Relative abundance (", taxrank, " > ", show*100, "%)")
			}
			self$use_percentage <- use_percentage
			self$ylabname <- ylabname
			self$taxrank <- taxrank
			self$data_abund <- abund_data
			self$data_taxanames <- use_taxanames
			self$high_level <- high_level
			message('The transformed abundance data is stored in object$data_abund ...')
		},
		#' @description
		#' Bar plot.
		#'
		#' @param color_values default \code{RColorBrewer::brewer.pal}(8, "Dark2"); colors palette for the bars.
		#' @param bar_full default TRUE; Whether the bar shows all the features (including 'Others'). 
		#'    Default \code{TRUE} means total abundance are summed to 1 or 100 (percentage). \code{FALSE} means 'Others' will not be shown.
		#' @param others_color default "grey90"; the color for "Others" taxa.
		#' @param facet default NULL; a character vector for the facet; group column name of \code{sample_table}, such as, \code{"Group"};
		#'    If multiple facets are needed, please provide ordered names, such as \code{c("Group", "Type")}.
		#'    The latter should have a finer scale than the former one;
		#'    Please adjust the facet orders in the plot by assigning factors in \code{sample_table} before creating \code{trans_abund} object or 
		#'    assigning factors in the \code{data_abund} table of \code{trans_abund} object.
		#'    When multiple facets are used, please first install package \code{ggh4x} using the command \code{install.packages("ggh4x")}.
		#' @param order_x default NULL; vector; used to order the sample names in x axis; must be the samples vector, such as \code{c("S1", "S3", "S2")}.
		#' @param x_axis_name NULL; a character string; a column name of sample_table in dataset; used to show the sample names in x axis.
		#' @param barwidth default 0.9; bar width, see \code{width} in \code{geom_bar} of ggplot2 package.
		#' @param use_alluvium default FALSE; whether add alluvium plot. If \code{TRUE}, please first install \code{ggalluvial} package.
		#' @param clustering default FALSE; whether order samples by the clustering.
		#' @param clustering_plot default FALSE; whether add clustering plot.
		#'     If \code{clustering_plot = TRUE}, \code{clustering} will be also TRUE in any case for the clustering.
		#' @param cluster_plot_width default 0.2, the dendrogram plot width; available when \code{clustering_plot = TRUE}.
		#' @param facet_color default "grey95"; facet background color.
		#' @param strip_text default 11; facet text size.
		#' @param legend_text_italic default FALSE; whether use italic in legend.
		#' @param xtext_angle default 0; number ranging from 0 to 90; used to adjust x axis text angle to reduce text overlap; 
		#' @param xtext_size default 10; x axis text size.
		#' @param xtext_keep default TRUE; whether to keep the text on the x-axis.
		#' @param xtitle_keep default TRUE; whether to keep the title of the x-axis.
		#' @param ytitle_size default 17; y axis title size.
		#' @param coord_flip default FALSE; whether flip cartesian coordinates so that horizontal becomes vertical, and vertical becomes horizontal.
		#' @param ggnested default FALSE; whether use nested legend. Need \code{ggnested} package to be installed (https://github.com/gmteunisse/ggnested).
		#'   To make it available, please assign \code{high_level} parameter when creating the object.
		#' @param high_level_add_other default FALSE; whether add 'Others' (all the unknown taxa) in each taxon of higher taxonomic level.
		#'   Only available when \code{ggnested = TRUE}.
		#' @param sample_plot default NULL; Use the heatmap colors to represent sample information. 
		#'   The input should be column names from \code{sample_table}, e.g., \code{c("Group", "pH")}.
		#' @param sample_plot_color default NULL; Color settings. The input must be a list that corresponds to \code{sample_plot}, 
		#'   e.g. \code{list(Group = RColorBrewer::brewer.pal(6, "Set2"), pH = c("white", "red"))}.
		#'   When the input factor is a numerical variable, it will be displayed with a color gradient; 
		#'   therefore, two colors should be provided for the input (as shown for "pH" above).
		#' @param sample_plot_height default NULL; Height of the sample heatmap; defaults to 1/10 of the main bar plot. 
		#'   The input must be a vector whose length equals that of \code{sample_plot}, e.g., \code{c(0.1, 0.1)}.
		#' @param sample_plot_mainnames default FALSE; whether show the sample names in the main bar plot.
		#' @param bar_type deprecated. Please use \code{bar_full} argument instead.
		#' @return ggplot2 object. 
		#' @examples
		#' \donttest{
		#' t1$plot_bar(facet = "Group", xtext_keep = FALSE)
		#' }
		plot_bar = function(
			color_values = RColorBrewer::brewer.pal(8, "Dark2"),
			bar_full = TRUE,
			others_color = "grey90",
			facet = NULL,
			order_x = NULL,
			x_axis_name = NULL,
			barwidth = 0.9,
			use_alluvium = FALSE,
			clustering = FALSE,
			clustering_plot = FALSE,
			cluster_plot_width = 0.2,
			facet_color = "grey95",
			strip_text = 11,
			legend_text_italic = FALSE,
			xtext_angle = 0,
			xtext_size = 10,
			xtext_keep = TRUE,
			xtitle_keep = TRUE,
			ytitle_size = 17,
			coord_flip = FALSE,
			ggnested = FALSE,
			high_level_add_other = FALSE,
			sample_plot = NULL, 
			sample_plot_color = NULL, 
			sample_plot_height = NULL,
			sample_plot_mainnames = FALSE,
			bar_type = deprecated()
			){
			
			if(lifecycle::is_present(bar_type)) {
				lifecycle::deprecate_warn("1.7.0", "plot_bar(bar_type)", "plot_bar(bar_full)")
				if(bar_type == "full"){
					bar_full <- TRUE
				}else{
					bar_full <- FALSE
				}
			}
			
			plot_data <- self$data_abund
			# try to filter useless columns
			plot_data %<>% .[, ! colnames(.) %in% c("N", "SD", "SE", "Median", "Min", "Max", "quantile25", "quantile75", "all_mean_abund")]
			use_taxanames <- self$data_taxanames
			if(ggnested){
				if(is.null(self$high_level)){
					stop("The high_level is necessary when ggnested = TRUE! Please assign high_level parameter when creating the object!")
				}
				if(high_level_add_other){
					plot_data$Taxonomy[!plot_data$Taxonomy %in% use_taxanames] <- "Others"
					use_taxanames %<>% c(., "Others")
					new_data <- plot_data %>% dplyr::group_by(!!! syms(c(self$high_level, "Taxonomy", "Sample"))) %>% 
						dplyr::summarise(Abundance = sum(Abundance)) %>%
						as.data.frame(stringsAsFactors = FALSE)
					plot_data_merge <- plot_data[, ! colnames(plot_data) %in% c(self$high_level, "Taxonomy", "Abundance"), drop = FALSE] %>% unique
					plot_data <- dplyr::left_join(new_data, plot_data_merge, by = c("Sample" = "Sample"))
				}
				bar_full <- FALSE
			}
			if(bar_full){
				# make sure that taxonomy info are all in selected use_taxanames in case of special data
				if(!all(plot_data$Taxonomy %in% use_taxanames)){
					plot_data$Taxonomy[!plot_data$Taxonomy %in% use_taxanames] <- "Others"
					new_data <- plot_data %>% dplyr::group_by(!!! syms(c("Taxonomy", "Sample"))) %>% 
						dplyr::summarise(Abundance = sum(Abundance)) %>%
						as.data.frame(stringsAsFactors = FALSE)
					plot_data_merge <- plot_data[, ! colnames(plot_data) %in% c("Taxonomy", "Abundance"), drop = FALSE] %>% unique
					plot_data <- dplyr::left_join(new_data, plot_data_merge, by = c("Sample" = "Sample"))
					plot_data$Taxonomy %<>% factor(., levels = rev(c(use_taxanames, "Others")))
				}else{
					plot_data$Taxonomy %<>% factor(., levels = rev(use_taxanames))
				}
			}else{
				if(ggnested){
					plot_data %<>% .[.[, self$high_level] %in% self$data_taxanames_highlevel, ]
					plot_data[, self$high_level] %<>% factor(., levels = self$data_taxanames_highlevel)
				}
				plot_data %<>% {.[.$Taxonomy %in% use_taxanames, ]}
				# two legend ordering types depending on ggnested
				if(ggnested){
					plot_data$Taxonomy %<>% factor(., levels = use_taxanames)
				}else{
					plot_data$Taxonomy %<>% factor(., levels = rev(use_taxanames))
				}
			}
			# order x axis samples
			plot_data <- private$adjust_axis_facet(
				plot_data = plot_data, 
				x_axis_name = x_axis_name, 
				order_x = order_x
				)
			# arrange plot_data--Abundance according to the Taxonomy-group column factor-levels
			plot_data <- plot_data[unlist(lapply(levels(plot_data$Taxonomy), function(x) which(plot_data$Taxonomy == x))),]
			if(!ggnested){
				if(any(grepl("Others", as.character(plot_data$Taxonomy)))){
					bar_colors_use <- expand_colors(color_values, length(unique(plot_data$Taxonomy)) - 1)
					bar_colors_use <- c(bar_colors_use, others_color)
				}else{
					bar_colors_use <- expand_colors(color_values, length(unique(plot_data$Taxonomy)))
				}
			}else{
				# high_level determine the colors
				bar_colors_use <- expand_colors(color_values, length(unique(plot_data[, self$high_level])))
			}
			if(clustering | clustering_plot){
				data_clustering <- reshape2::dcast(plot_data, Sample ~ Taxonomy, value.var = "Abundance", fun.aggregate = sum) %>% 
					`row.names<-`(.[,1]) %>% .[, -1]
				tmp_hclust <- hclust(dist(data_clustering)) 
				order_x_clustering <- tmp_hclust %>% {.$labels[.$order]} %>% as.character
				plot_data$Sample %<>% factor(., levels = order_x_clustering)
			}
			if(use_alluvium){
				p <- ggplot(plot_data, aes(
						x = Sample, y = Abundance, 
						fill = Taxonomy, color = Taxonomy, 
						weight = Abundance, 
						alluvium = Taxonomy, stratum = Taxonomy
					)) +
					ggalluvial::geom_flow(alpha = .4, width = 3/15) +
					ggalluvial::geom_stratum(width = .2) +
					scale_color_manual(values = rev(bar_colors_use))
			}else{
				if(ggnested){
					p <- ggnested::ggnested(plot_data, aes_meco(x = "Sample", y = "Abundance", main_group = self$high_level, sub_group = "Taxonomy"), main_palette = bar_colors_use)
				}else{
					p <- ggplot(plot_data, aes_meco(x = "Sample", y = "Abundance", fill = "Taxonomy"))
				}
				if(bar_full){
					if(self$use_percentage == T){
						p <- p + geom_bar(stat = "identity", position = "stack", show.legend = T, width = barwidth)
					}else{
						p <- p + geom_bar(stat = "identity", position = "fill", show.legend = T, width = barwidth)
					}
				}else{
					p <- p + geom_bar(stat = "identity", position = "stack", show.legend = T, width = barwidth)
				}
			}
			if(!ggnested){
				p <- p + scale_fill_manual(values = rev(bar_colors_use))
			}
			p <- p + xlab("") + ylab(self$ylabname)
			if(!is.null(facet)){
				if(coord_flip){
					facet_formula <- reformulate(".", paste0(facet, collapse = " + "))
				}else{
					facet_formula <- reformulate(facet, ".")
				}
				if(length(facet) == 1){
					p <- p + facet_grid(facet_formula, scales = "free", space = "free")
				}else{
					p <- p + ggh4x::facet_nested(facet_formula, nest_line = element_line(linetype = 2), scales = "free", space = "free")
				}
				p <- p + theme(strip.background = element_rect(fill = facet_color, color = facet_color), strip.text = element_text(size=strip_text))
				p <- p + scale_y_continuous(expand = c(0, 0.01))
			}else{
				if(bar_full & self$use_percentage == FALSE){
					p <- p + scale_y_continuous(limits = c(0, 1), expand = c(0, 0))
				}else{
					p <- p + scale_y_continuous(expand = c(0, 0))
				}
			}
			p <- p + theme(panel.grid = element_blank(), panel.border = element_blank()) + 
				theme(axis.line.y = element_line(color = "grey60", linetype = "solid", lineend = "square"))
			if(legend_text_italic == T) {
				p <- p + theme(legend.text = element_text(face = 'italic'))
			}
			if(clustering_plot){
				if(! coord_flip){
					message("Rotate the axis automatically to add the clustering plot ...")
					coord_flip <- TRUE
				}
			}
			p <- p + private$ggplot_xtext_type(xtext_angle = xtext_angle, xtext_size = xtext_size, xtext_keep = xtext_keep, coord_flip = coord_flip)
			p <- p + theme(axis.title.y = element_text(size = ytitle_size))
			if(xtitle_keep == F){
				p <- p + theme(axis.title.x = element_blank())
			}
			p <- p + guides(fill = guide_legend(title = self$taxrank))
			if(use_alluvium | ggnested){
				p <- p + guides(color = guide_legend(title = self$taxrank))
			}
			if(coord_flip){
				p <- p + coord_flip()
			}
			if(clustering_plot){
				left_plot <- ggtree::ggtree(tmp_hclust, hang = 0)
				p %<>% aplot::insert_left(left_plot, width = cluster_plot_width)
			}
			if(!is.null(sample_plot)){
				if(coord_flip){
					stop("The sample_plot cannot be applied currently when using coord_flip!")
				}
				if(!is.vector(sample_plot)){
					stop("Input sample_plots parameter must be a vector!")
				}
				if(!sample_plot_mainnames){
					p <- p + theme(
						axis.text.x = element_blank(),
						axis.ticks.x = element_blank(),
						axis.title.x = element_blank()
						)
				}
				
				metadata_table <- plot_data[plot_data$Taxonomy == use_taxanames[1], ]
				
				sample_plot_list <- list()
				for(i in sample_plot){
					tmp <- metadata_table[, c("Sample", i)]
					tmp2 <- reshape2::melt(tmp, id.vars = "Sample", value.name = "Value")
					if(!is.null(facet)){
						tmp2 <- dplyr::left_join(tmp2, metadata_table[, c("Sample", facet)], by = c("Sample" = "Sample"))
					}
					g1 <- ggplot(tmp2, aes(x = Sample, y = variable, fill = Value)) +
						geom_tile()
					if(is.numeric(tmp[, i])){
						if(!is.null(sample_plot_color)){
							g1 <- g1 + scale_fill_gradient2(low = sample_plot_color[[i]][1], high = sample_plot_color[[i]][2])
						}else{
							g1 <- g1 + scale_fill_gradient2()
						}
					}else{
						if(!is.null(sample_plot_color)){
							g1 <- g1 + scale_fill_manual(values = sample_plot_color[[i]])
						}else{
							g1 <- g1 + scale_fill_discrete()
						}
					}
					if(!is.null(facet)){
						if(length(facet) == 1){
							g1 <- g1 + facet_grid(facet_formula, scales = "free", space = "free")
						}else{
							g1 <- g1 + ggh4x::facet_nested(facet_formula, nest_line = element_line(linetype = 2), scales = "free", space = "free")
						}
					}
					g1 <- g1 + theme_minimal() +
						labs(fill = i) + 
						theme(
							axis.text.x = element_blank(),
							axis.ticks.x = element_blank(),
							axis.title.x = element_blank(),
							axis.title.y = element_blank(),
							panel.grid = element_blank(),
							strip.background = element_blank(),
							strip.text       = element_blank(),
							panel.border     = element_blank(),
							panel.background = element_blank(),
							legend.position = "right"
						)
					sample_plot_list[[i]] <- g1
				}
				if(is.null(sample_plot_height)){
					sample_plot_height <- rep(0.1, length(sample_plot))
				}

				p <- patchwork::wrap_plots(c(list(p), sample_plot_list), byrow = TRUE) +
					patchwork::plot_layout(ncol = 1, axes = "collect", heights = c(1, sample_plot_height))
			
			}
			p
		},
		#' @description
		#' Plot the heatmap.
		#'
		#' @param color_values default rev(RColorBrewer::brewer.pal(n = 11, name = "RdYlBu")); 
		#' 	  colors palette for the plotting.
		#' @param facet default NULL; a character vector for the facet; a group column name of \code{sample_table}, such as, \code{"Group"};
		#'    If multiple facets are needed, please provide ordered names, such as \code{c("Group", "Type")}.
		#'    The latter should have a finer scale than the former one;
		#'    Please adjust the facet orders in the plot by assigning factors in \code{sample_table} before creating \code{trans_abund} object or 
		#'    assigning factors in the \code{data_abund} table of \code{trans_abund} object.
		#'    When multiple facets are used, please first install package \code{ggh4x} using the command \code{install.packages("ggh4x")}.
		#' @param facet_switch default "y"; By default, the labels in facets are displayed on the top and right of the plot. 
		#'    If "x", the top labels will be displayed to the bottom. If "y", the right-hand side labels will be displayed to the left. Can also be set to "both".
		#'    When the \code{high_level} is found in the object, the function will generate facets for the higher taxonomy in y axis.
		#'    So the default "y" of the parameter is to make the visualization better when \code{high_level} is found.
		#'    This parameter will be passed to the \code{switch} parameter in \code{ggplot2::facet_grid} or \code{ggh4x::facet_nested} function.		
		#' @param x_axis_name NULL; a character string; a column name of sample_table used to show the sample names in x axis.
		#' @param order_x default NULL; vector; used to order the sample names in x axis; must be the samples vector, such as, c("S1", "S3", "S2").
		#' @param withmargin default TRUE; whether retain the tile margin.
		#' @param plot_numbers default FALSE; whether plot the number in heatmap.
		#' @param plot_text_size default 4; If plot_numbers TRUE, text size in plot.
		#' @param plot_breaks default NULL; The legend breaks.
		#' @param margincolor default "white"; If withmargin TRUE, use this as the margin color.
		#' @param plot_colorscale default "log10"; color scale.
		#' @param min_abundance default .01; the minimum abundance percentage in plot.
		#' @param max_abundance default NULL; the maximum abundance percentage in plot, NULL reprensent the max percentage.
		#' @param strip_text default 11; facet text size.
		#' @param xtext_keep default TRUE; whether to keep the text on the x-axis.
		#' @param xtext_angle default 0; number ranging from 0 to 90; used to adjust x axis text angle to reduce text overlap; 
		#' @param xtext_size default 10; x axis text size.
		#' @param ytext_size default 11; y axis text size.
		#' @param xtitle_keep default TRUE; whether to keep the title of the x-axis.
		#' @param grid_clean default TRUE; whether remove grid lines.
		#' @param legend_title default "\% Relative\\nAbundance"; legend title text.
		#' @param pheatmap default FALSE; whether use pheatmap package to plot the heatmap.
		#' @param ... paremeters pass to pheatmap when pheatmap = TRUE.
		#' @return ggplot2 object or grid object based on pheatmap.
		#' @examples
		#' \donttest{
		#' t1 <- trans_abund$new(dataset = dataset, taxrank = "Genus", ntaxa = 40)
		#' t1$plot_heatmap(facet = "Group", xtext_keep = FALSE, withmargin = FALSE)
		#' }
		plot_heatmap = function(
			color_values = rev(RColorBrewer::brewer.pal(n = 11, name = "RdYlBu")), 
			facet = NULL,
			facet_switch = "y",
			x_axis_name = NULL,
			order_x = NULL,
			withmargin = TRUE,
			plot_numbers = FALSE,
			plot_text_size = 4,
			plot_breaks = NULL,
			margincolor = "white",
			plot_colorscale = "log10",
			min_abundance = 0.01,
			max_abundance = NULL,
			strip_text = 11,
			xtext_keep = TRUE,
			xtext_angle = 0,
			xtext_size = 10,
			ytext_size = 11,
			xtitle_keep = TRUE,
			grid_clean = TRUE,
			legend_title = "% Relative\nAbundance",
			pheatmap = FALSE,
			...
			){
			plot_data <- self$data_abund
			use_taxanames <- self$data_taxanames
			plot_data %<>% {.[.$Taxonomy %in% use_taxanames, ]}

			if(pheatmap == FALSE){
				# order x axis samples
				plot_data <- private$adjust_axis_facet(plot_data = plot_data, x_axis_name = x_axis_name, order_x = order_x)
				if (is.null(min_abundance)){
					min_abundance <- ifelse(min(plot_data$Abundance) > 0.001, min(plot_data$Abundance), 0.001)
				}
				if (is.null(max_abundance)){
					max_abundance <- max(plot_data$Abundance)
				}
				plot_data$Taxonomy %<>% factor(., levels = rev(use_taxanames))

				p <- ggplot(plot_data, aes(x = .data[["Sample"]], y = .data[["Taxonomy"]], label = .data[[formatC("Abundance", format = "f", digits = 1)]]))
				
				if(withmargin == T){
					p <- p + geom_tile(aes(fill = Abundance), colour = margincolor, size = 0.5)
				}else{
					p <- p + geom_tile(aes(fill = Abundance))
				}
				p <- p + theme(axis.text.y = element_text(size = 12)) + theme(plot.margin = unit(c(0.3, 0.3, 0.3, 0.3), "cm"))

				if (plot_numbers == T){
					abund <- plot_data
					abund$Abundance <- round(abund$Abundance, 1)
					p <- p + geom_text(data = abund, size = plot_text_size, colour = "grey10")  
				}
				if (is.null(plot_breaks)){
					p <- p + scale_fill_gradientn(colours = color_values, trans = plot_colorscale, na.value = "#00008B", limits = c(min_abundance, max_abundance))
				}else{
					p <- p + scale_fill_gradientn(colours = color_values, trans = plot_colorscale, breaks = plot_breaks, na.value = "#00008B",
						limits = c(min_abundance, max_abundance))
				}
				if(!is.null(facet) | !is.null(self$high_level)){
					if(is.null(self$high_level)){
						y_facet <- "."
					}else{
						y_facet <- self$high_level
						if(is.null(facet)){
							facet <- "."
						}
					}
					facet_formula <- reformulate(facet, y_facet)
					if(length(facet) == 1){
						p <- p + facet_grid(facet_formula, scales = "free", space = "free", switch = facet_switch)
					}else{
						p <- p + ggh4x::facet_nested(facet_formula, nest_line = element_line(linetype = 2), scales = "free", space = "free", switch = facet_switch)
					}
					p <- p + theme(strip.background = element_rect(color = "white", fill = "grey92"), strip.text = element_text(size = strip_text))
					p <- p + theme(strip.text.y.left = element_text(angle = 360), strip.text.y.right = element_text(angle = 360))
					if(!is.null(self$high_level)){
						if(!is.null(facet_switch)){
							if(facet_switch != "x"){
								p <- p + scale_y_discrete(position = "right")
							}
						}
					}
				}
				p <- p + labs(x = "", y = "", fill = legend_title)
				if (!is.null(ytext_size)){
					p <- p + theme(axis.text.y = element_text(size = ytext_size))
				}
				p <- p + private$ggplot_xtext_type(xtext_angle = xtext_angle, xtext_size = xtext_size, xtext_keep = xtext_keep)
				if(grid_clean){
					p <- p + theme(panel.border = element_blank(), panel.grid = element_blank())
				}
				p
			} else {
				# first to wide format
				wide_table <- reshape2::dcast(plot_data, Taxonomy ~ Sample, value.var = "Abundance") %>% 
					`row.names<-`(.[,1]) %>% 
					.[, -1, drop = FALSE]
				# check sd for each feature, if 0, delete
				if(any(apply(wide_table, MARGIN = 1, FUN = function(x) sd(x) == 0))){
					select_rows <- apply(wide_table, MARGIN = 1, FUN = function(x) sd(x) != 0)
					wide_table %<>% {.[select_rows, ]}
				}
				p <- pheatmap::pheatmap(
					wide_table,
					...
					)
				p$gtable
			}
		},
		#' @description
		#' Box plot.
		#'
		#' @param color_values default \code{RColorBrewer::brewer.pal}(8, "Dark2"); colors palette for the box.
		#' @param group default NULL; a column name of sample table to show abundance across groups.
		#' @param show_point default FALSE; whether show points in plot.
		#' @param point_color default "black"; If show_point TRUE; use the color
		#' @param point_size default 3; If show_point TRUE; use the size
		#' @param point_alpha default .3; If show_point TRUE; use the transparency.
		#' @param plot_flip default FALSE; Whether rotate plot.
		#' @param boxfill default TRUE; Whether fill the box with colors.
		#' @param middlecolor default "grey95"; The middle line color.
		#' @param middlesize default 1; The middle line size.
		#' @param xtext_angle default 0; number ranging from 0 to 90; used to adjust x axis text angle to reduce text overlap; 
		#' @param xtext_size default 10; x axis text size.
		#' @param ytitle_size default 17; y axis title size.
		#' @param ... parameters pass to \code{geom_boxplot} function.
		#' @return ggplot2 object. 
		#' @examples
		#' \donttest{
		#' t1$plot_box(group = "Group")
		#' }
		plot_box = function(
			color_values = RColorBrewer::brewer.pal(8, "Dark2"),
			group = NULL,
			show_point = FALSE,
			point_color = "black",
			point_size = 3,
			point_alpha = .3,
			plot_flip = FALSE,
			boxfill = TRUE,
			middlecolor = "grey95",
			middlesize = 1,
			xtext_angle = 0,
			xtext_size = 10,
			ytitle_size = 17,
			...
			){
			plot_data <- self$data_abund
			use_taxanames <- self$data_taxanames

			plot_data %<>% {.[.$Taxonomy %in% use_taxanames, ]}
			plot_data$Taxonomy %<>% factor(., levels = use_taxanames)

			p <- ggplot(plot_data, aes(x = .data[["Taxonomy"]], y = .data[["Abundance"]])) 
			p <- p + ylab(self$ylabname) + guides(col = guide_legend(reverse = TRUE)) + xlab("")
			if (plot_flip == T){ 
				p <- p + coord_flip()
			}
			if(is.null(group)) {
				p <- p + geom_boxplot(color = color_values[1], ...)
			} else {
				color_values <- expand_colors(color_values, length(unique(plot_data[, group])))
				if(boxfill == T){
					p <- p + geom_boxplot(aes(color = .data[[group]], fill = .data[[group]]), ...)
					p <- p + scale_fill_manual(values = color_values)
					p <- p + scale_color_manual(values = color_values) + guides(color = "none")
					## Change the default middle line
					dat <- ggplot_build(p)$data[[1]]
					p <- p + geom_segment(data=dat, aes(x = xmin, xend = xmax, y = middle, yend = middle), colour = middlecolor, linewidth = middlesize)
				} else {
					p <- p + geom_boxplot(aes(color = .data[[group]]), ...) + scale_color_manual(values = color_values)
				}
			}
			if(show_point == T){
				p <- p + geom_point(size = point_size, color = point_color, alpha = point_alpha, position = "jitter")
			}
			p <- p + private$ggplot_xtext_type(xtext_angle = xtext_angle, xtext_size = xtext_size)
			p <- p + theme(axis.title.y = element_text(size = ytitle_size)) + scale_y_continuous(expand = c(0, 0.01))

			if(!is.null(group)) {
				p <- p + guides(fill=guide_legend(title=group))
			}
			p
		},
		#' @description
		#' Plot the line chart.
		#'
		#' @param color_values default \code{RColorBrewer::brewer.pal}(8, "Dark2"); colors palette for the points and lines.
		#' @param plot_SE default TRUE; TRUE: the errorbar is \eqn{mean±se}; FALSE: the errorbar is \eqn{mean±sd}.
		#' @param position default position_dodge(0.1); Position adjustment, either as a string (such as "identity"), or the result of a call to a position adjustment function.
		#' @param errorbar_size default 1; errorbar line size.
		#' @param errorbar_width default 0.1; errorbar width.
		#' @param point_size default 3; point size for taxa.
		#' @param point_alpha default 0.8; point transparency.
		#' @param line_size default 0.8; line size.
		#' @param line_alpha default 0.8; line transparency.
		#' @param line_type default 1; an integer; line type.
		#' @param xtext_angle default 0; number ranging from 0 to 90; used to adjust x axis text angle to reduce text overlap; 
		#' @param xtext_size default 10; x axis text size.
		#' @param ytitle_size default 17; y axis title size.
		#' @return ggplot2 object. 
		#' @examples
		#' \donttest{
		#' t1 <- trans_abund$new(dataset = dataset, taxrank = "Genus", ntaxa = 5)
		#' t1$plot_line(point_size = 3)
		#' t1 <- trans_abund$new(dataset = dataset, taxrank = "Genus", ntaxa = 5, groupmean = "Group")
		#' t1$plot_line(point_size = 5, errorbar_size = 1, xtext_angle = 30)
		#' }
		plot_line = function(
			color_values = RColorBrewer::brewer.pal(8, "Dark2"),
			plot_SE = TRUE,
			position = position_dodge(0.1),
			errorbar_size = 1,
			errorbar_width = 0.1,
			point_size = 3,
			point_alpha = 0.8,
			line_size = 0.8, 
			line_alpha = 0.8, 
			line_type = 1,
			xtext_angle = 0,
			xtext_size = 10,
			ytitle_size = 17
			){
			plot_data <- self$data_abund
			use_taxanames <- self$data_taxanames
			plot_data %<>% {.[.$Taxonomy %in% use_taxanames, ]}
			plot_data$Taxonomy %<>% factor(., levels = use_taxanames)
			color_values <- expand_colors(color_values, length(use_taxanames))
			
			p <- ggplot(plot_data, aes(x = .data[["Sample"]], y = .data[["Abundance"]], color = .data[["Taxonomy"]], group = .data[["Taxonomy"]]))
			if(("SE" %in% colnames(plot_data)) & plot_SE){
				p <- p + geom_errorbar(aes(ymin = Abundance - SE, ymax = Abundance + SE), width = errorbar_width, position = position, linewidth = errorbar_size)
			}else{
				if(("SD" %in% colnames(plot_data)) & plot_SE){
					p <- p + geom_errorbar(aes(ymin = Abundance - SD, ymax = Abundance + SD), width = errorbar_width, position = position, linewidth = errorbar_size)
				}
			}
			p <- p + geom_point(size = point_size, alpha = point_alpha, position = position)
			p <- p + geom_line(size = line_size, alpha = line_alpha, linetype = line_type, position = position)
			p <- p + ylab(self$ylabname) + guides(col = guide_legend(title=self$taxrank, reverse = TRUE)) + xlab("")
			p <- p + private$ggplot_xtext_type(xtext_angle = xtext_angle, xtext_size = xtext_size)
			p <- p + theme(axis.title.y = element_text(size = ytitle_size)) + scale_y_continuous(expand = c(0, 0.01))
			p <- p + scale_color_manual(values = color_values)
			p
		},
		#' @description
		#' Pie chart.
		#'
		#' @param color_values default \code{RColorBrewer::brewer.pal}(8, "Dark2"); colors palette for each section.
		#' @param facet_nrow default 1; how many rows in the plot.
		#' @param strip_text default 11; sample title size.
		#' @param add_label default FALSE; Whether add the percentage label in each section of pie chart.
		#' @param legend_text_italic default FALSE; whether use italic in legend.
		#' @return ggplot2 object. 
		#' @examples
		#' \donttest{
		#' t1 <- trans_abund$new(dataset = dataset, taxrank = "Phylum", ntaxa = 6, groupmean = "Group")
		#' t1$plot_pie(facet_nrow = 1)
		#' }
		plot_pie = function(
			color_values = RColorBrewer::brewer.pal(8, "Dark2"), 
			facet_nrow = 1, 
			strip_text = 11, 
			add_label = FALSE,
			legend_text_italic = FALSE
			){
			plot_data <- self$data_abund
			use_taxanames <- self$data_taxanames
			# sum others to one
			if(any(!plot_data$Taxonomy %in% use_taxanames)){
				plot_data$Taxonomy[!plot_data$Taxonomy %in% use_taxanames] <- "Others"
				plot_data %<>% dplyr::group_by(!!! syms(c("Taxonomy", "Sample"))) %>% 
					dplyr::summarise(Abundance = sum(Abundance)) %>%
					as.data.frame(stringsAsFactors = FALSE)
				plot_data$Taxonomy %<>% factor(., levels = c(use_taxanames, "Others"))
				color_values <- expand_colors(color_values, length(use_taxanames) + 1)
			}else{
				color_values <- expand_colors(color_values, length(use_taxanames))
			}
			plot_data$label <- paste0(round(plot_data$Abundance, 1), "%")
			p <- ggplot(plot_data, aes(x = '', y = Abundance, fill = Taxonomy, label = label)) + 
				geom_bar(width = 1, stat = "identity") +
				coord_polar("y", start = 0)
			if(add_label){
				p <- p + ggrepel::geom_label_repel(position = position_stack(vjust = 0.5), show.legend = FALSE)
			}
			p <- p + private$blank_theme() +
				scale_fill_manual(values = color_values) +
				theme(axis.text.x = element_blank()) +
				facet_wrap(~Sample, nrow = facet_nrow) +
				theme(strip.text = element_text(size = strip_text)) +
				guides(fill = guide_legend(title = self$taxrank))
			if(legend_text_italic == T) {
				p <- p + theme(legend.text = element_text(face = 'italic'))
			}
			p
		},
		#' @description
		#' Donut chart based on the \code{ggpubr::ggdonutchart} function.
		#'
		#' @param color_values default \code{RColorBrewer::brewer.pal}(8, "Dark2"); colors palette for the donut.
		#' @param label default TRUE; whether show the percentage label.
		#' @param facet_nrow default 1; how many rows in the plot.
		#' @param legend_text_italic default FALSE; whether use italic in legend.
		#' @param ... parameters passed to \code{ggpubr::ggdonutchart}.
		#' @return combined ggplot2 objects list, generated by \code{ggpubr::ggarrange} function. 
		#' @examples
		#' \dontrun{
		#' t1 <- trans_abund$new(dataset = dataset, taxrank = "Phylum", ntaxa = 6, groupmean = "Group")
		#' t1$plot_donut(label = TRUE)
		#' }
		plot_donut = function(
			color_values = RColorBrewer::brewer.pal(8, "Dark2"), 
			label = TRUE,
			facet_nrow = 1, 
			legend_text_italic = FALSE,
			...
			){
			plot_data <- self$data_abund
			use_taxanames <- self$data_taxanames
			# sum others to one
			if(any(!plot_data$Taxonomy %in% use_taxanames)){
				plot_data$Taxonomy[!plot_data$Taxonomy %in% use_taxanames] <- "Others"
				plot_data %<>% dplyr::group_by(!!! syms(c("Taxonomy", "Sample"))) %>% 
					dplyr::summarise(Abundance = sum(Abundance)) %>%
					as.data.frame(stringsAsFactors = FALSE)
				plot_data$Taxonomy %<>% factor(., levels = c(use_taxanames, "Others"))
				color_values <- expand_colors(color_values, length(use_taxanames) + 1)
			}else{
				color_values <- expand_colors(color_values, length(use_taxanames))
			}
			plot_data$label <- paste0(round(plot_data$Abundance, 1), "%")

			# use ggarrange, because facet can not seperate the labels of all the samples
			plot_list <- list()
			for(i in unique(plot_data$Sample)){
				tmp <- plot_data[plot_data$Sample == i, ]
				p <- ggpubr::ggdonutchart(tmp, "Abundance", fill = "Taxonomy", label = "label", color = "white", palette = color_values, ...) + 
					guides(fill = guide_legend(title=self$taxrank))
					theme(axis.text.y = element_blank())
				if(label == F){
					p <- p + theme(axis.text.x = element_blank())
				}
				if(legend_text_italic == T) {
					p <- p + theme(legend.text = element_text(face = 'italic'))
				}
				plot_list[[i]] <- p
			}
			facet_ncol <- ceiling(length(plot_list)/facet_nrow)
			ggpubr::ggarrange(plotlist = plot_list, nrow = facet_nrow, ncol = facet_ncol, labels = names(plot_list), common.legend = TRUE, legend = "bottom")
		},
		#' @description
		#' Radar chart based on the \code{ggradar} package (https://github.com/ricardo-bion/ggradar).
		#'
		#' @param color_values default \code{RColorBrewer::brewer.pal}(8, "Dark2"); colors palette for samples.
		#' @param ... parameters passed to \code{ggradar::ggradar} function except group.colours parameter.
		#' @return ggplot2 object. 
		#' @examples
		#' \dontrun{
		#' t1 <- trans_abund$new(dataset = dataset, taxrank = "Phylum", ntaxa = 6, groupmean = "Group")
		#' t1$plot_radar()
		#' }
		plot_radar = function(
			color_values = RColorBrewer::brewer.pal(8, "Dark2"),
			...
			){
			plot_data <- self$data_abund
			use_taxanames <- self$data_taxanames
			color_values <- expand_colors(color_values, length(use_taxanames))
			plot_data <- plot_data[plot_data$Taxonomy %in% use_taxanames, ]
			if(self$use_percentage){
				plot_data$Abundance %<>% {./100}
			}
			tmp_data <- reshape2::dcast(plot_data, Taxonomy ~ Sample, value.var = "Abundance")
			colnames(tmp_data)[1] <- "group"
			tmp_data$group %<>% factor(., levels = use_taxanames)
			# https://github.com/ricardo-bion/ggradar
			ggradar::ggradar(tmp_data, group.colours = color_values, ...)
		},
		#' @description
		#' Ternary diagrams based on the \code{ggtern} package.
		#'
		#' @param color_values default \code{RColorBrewer::brewer.pal}(8, "Dark2"); colors palette for the samples.
		#' @param color_legend_guide_size default 4; The size of legend guide for color.
		#' @return ggplot2 object. 
		#' @examples
		#' \dontrun{
		#' t1 <- trans_abund$new(dataset = dataset, taxrank = "Phylum", ntaxa = 6, groupmean = "Group")
		#' t1$plot_tern()
		#' }
		plot_tern = function(
			color_values = RColorBrewer::brewer.pal(8, "Dark2"),
			color_legend_guide_size = 4
			){
			data_abund <- self$data_abund
			use_taxanames <- self$data_taxanames
			if(length(unique(data_abund$Sample)) != 3){
				stop("Ternary diagrams can only be used for three samples!")
			}
			data_abund %<>% {.[.$Taxonomy %in% use_taxanames, ]}
			data_abund$Taxonomy %<>% factor(., levels = use_taxanames)
			plot_data <- reshape2::dcast(data_abund, Taxonomy + all_mean_abund ~ Sample, value.var = "Abundance")
			colnames(plot_data)[2] <- "Abundance"
			if(is.factor(data_abund$Sample)){
				sample_names <- levels(data_abund$Sample)
				if(length(sample_names) > 3){
					data_names <- colnames(plot_data)[3:ncol(plot_data)]
					if(all(data_names %in% sample_names)){
						sample_names %<>% .[. %in% data_names]
					}else{
						sample_names <- data_names
					}
				}
			}else{
				sample_names <- unique(data_abund$Sample)
			}
			color_values <- expand_colors(color_values, length(use_taxanames))

			p <- ggtern::ggtern(data = plot_data, aes(x = .data[[sample_names[1]]], y = .data[[sample_names[2]]], z = .data[[sample_names[3]]])) +
				theme_bw() +
				geom_point(aes(color = Taxonomy, size = Abundance)) +
				scale_color_manual(values = color_values) +
				scale_fill_manual(values = color_values) +
				guides(color = guide_legend(override.aes = list(size = color_legend_guide_size)))
			p
		},
		#' @description
		#' Print the trans_abund object.
		print = function(){
			cat("trans_abund object:\n")
			cat(paste("data_abund have", ncol(self$data_abund), "columns: ", paste0(colnames(self$data_abund), collapse = ", "), "\n"))
			cat(paste("data_abund have total", length(unique(as.character(self$data_abund$Taxonomy))), "taxa\n"))
			cat(paste("taxrank: ", self$taxrank, "\n"))
			if(!is.null(self$data_taxanames)){
				if(length(self$data_taxanames) > 50){
					cat(paste("data_taxanames: ", length(self$data_taxanames), "taxa\n"))
				}else{
					cat(paste("data_taxanames: ", paste0(self$data_taxanames, collapse = ", "), "\n"))
				}
			}
			invisible(self)
		}
	),
	private = list(
		adjust_axis_facet = function(plot_data, x_axis_name, order_x){
			# order x axis samples and facet
			if(!is.null(x_axis_name)){
				colnames(plot_data)[colnames(plot_data) == "Sample"] <- "Sample_rownames_before"
				if(! x_axis_name %in% colnames(plot_data)){
					stop(paste("No", x_axis_name, "found in the column names of sample_table!"))
				}else{
					colnames(plot_data)[colnames(plot_data) == x_axis_name] <- "Sample"
				}
			}
			if(!is.null(order_x)){
				if(length(order_x) == 1){
					stop("This may be wrong. Only one sample used to order the samples!")
				}else{
					if(!all(unique(plot_data$Sample) %in% order_x)){
						message("Some sample names are not found in provided order_x parameter! This will cause the results in the figure to be abnormal!")
					}
					plot_data$Sample %<>% factor(., levels = order_x)
				}
			}
			plot_data
		},
		ggplot_xtext_type = function(xtext_angle, xtext_size, xtext_keep = TRUE, coord_flip = FALSE){
			if(coord_flip){
				if(xtext_keep){
					theme(axis.text.y = element_text(colour = "black", size = xtext_size))
				}else{
					theme(axis.ticks.y = element_blank(), axis.text.y = element_blank())
				}
			}else{
				if(xtext_keep){
					ggplot_xtext_anglesize(xtext_angle, xtext_size)
				}else{
					theme(axis.ticks.x = element_blank(), axis.text.x = element_blank())
				}
			}
		},
		blank_theme = function(){
			theme_minimal() +
			theme(
				axis.title = element_blank(),
				panel.border = element_blank(),
				panel.grid = element_blank(),
				axis.ticks = element_blank(),
				legend.position = "right",
				plot.title = element_text(size = 14, face = "bold")
			)
		}
	),
	lock_objects = FALSE,
	lock_class = FALSE
)
